The Hierarchical Structure of Galactic Haloes: Generalised N-Dimensional Clustering with CluSTAR-ND

We present CluSTAR-ND, a fast hierarchical galaxy/(sub)halo finder that produces Clustering Structure via Transformative Aggregation and Rejection in N-Dimensions. It is designed to improve upon Halo-OPTICS – an algorithm that automatically detects and extracts significant astrophysical clusters from the 3D spatial positions of simulation particles – by decreasing run-times, possessing the capability for metric adaptivity, and being readily applicable to data with any number of features. We directly compare these algorithms and find that not only does CluSTAR-ND produce a similarly robust clustering structure, it does so in a run-time that is at least 3 orders of magnitude faster. In optimising CluSTAR-ND’s clustering performance, we have also effectively made CluSTAR-ND a parameterless clustering algorithm since its parameters will be chosen automatically and optimally based on the input data. We conclude that CluSTAR-ND is a robust astrophysical clustering algorithm that can be leveraged to find stellar satellite groups on large synthetic or observational data sets.